Q01
Message
All templates
Chatbot-to-Agent Handoff CSAT & Experience Survey
Evaluates customer satisfaction and friction points during chatbot-to-human-agent transfers, measuring ease of transition, context retention, agent effectiveness, and reuse intent to guide support escalation optimization.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
22 questions · ~10 min
Q02
Multiple Choice
Where did this support conversation begin?
- Website chat widget
- Mobile app chat
- Messaging app (e.g., WhatsApp, Messenger)
- SMS/text
- Social media chat
- Voice/phone IVR with bot
- I don't remember
- Other (please specify)
Q03
Opinion Scale
How easy was the transition from chatbot to human agent?
Scale: 1 – 7
Min:Very difficultMax:Very easy
Q04
Opinion Scale
How effective was the human agent at addressing your issue after the handover?
Scale: 1 – 7
Min:Not at all effectiveMax:Extremely effective
Q05
Opinion Scale
Overall, how satisfied were you with this entire support experience (chatbot and human agent combined)?
Scale: 1 – 7
Min:Very dissatisfiedMax:Very satisfied
Q06
Long Text
What one change would have most improved the handover experience for you?
Q07
Dropdown
In which region do you currently live?
- Africa
- Asia
- Europe
- Latin America/Caribbean
- Middle East
- North America
- Oceania
- Prefer not to say
Q08
Message
Thank you for your time. Your feedback will directly help improve future chatbot-to-human handover experiences.
Q09
Dropdown
Approximately when did this handover occur?
- Within the last 7 days
- 8–14 days ago
- 15–30 days ago
- 31–90 days ago
- More than 90 days ago
- I'm not sure
Q10
Dropdown
Approximately how long did you wait between the chatbot and the human agent?
- No wait (immediate)
- Less than 1 minute
- 1–3 minutes
- 4–5 minutes
- 6–10 minutes
- 11–20 minutes
- More than 20 minutes
- I don't remember
Q11
Multiple Choice
Was your issue resolved by the end of the conversation?
- Yes, fully resolved
- Partially resolved
- No, not resolved
- Not applicable
Q12
Multiple Choice
Thinking about timing, would you have preferred the handover to happen…
- Sooner than it did
- Later than it did
- Timing was about right
- No preference
Q13
AI Interview
Based on your survey responses, we'd like to explore your handover experience in a bit more detail. Please share your thoughts openly—there are no right or wrong answers.
Q14
Dropdown
What is your age?
- 18–24
- 25–34
- 35–44
- 45–54
- 55–64
- 65+
- Prefer not to say
Q15
Multiple Choice
How did the transfer to a human agent happen?
- I asked to speak to a person
- The bot suggested transferring
- It happened automatically when the bot couldn't help
- I was offered a choice of agents or channels
- I'm not sure
Q16
Opinion Scale
To what extent did the human agent appear to have the context of your chatbot conversation (e.g., your issue, steps already taken)?
Scale: 1 – 7
Min:No context at allMax:Full context
Q17
Multiple Choice
What, if anything, did you have to repeat to the human agent? (Select all that apply)
- Name or account details
- Order/case number
- Problem description
- Steps already tried
- Files or screenshots
- Nothing had to be repeated
- Other (please specify)
Q18
Opinion Scale
How likely are you to use this chatbot again for future support needs?
Scale: 1 – 7
Min:Not at all likelyMax:Extremely likely
Q19
Multiple Choice
How do you describe your gender?
- Woman
- Man
- Non-binary
- Prefer not to say
Q20
Opinion Scale
How clearly were you informed about what would happen during the transfer (e.g., expected wait, what the agent would know)?
Scale: 1 – 7
Min:Not at all clearlyMax:Extremely clearly
Q21
Opinion Scale
How seamless did the overall handover feel?
Scale: 1 – 7
Min:Not at all seamlessMax:Completely seamless
Q22
Opinion Scale
Overall, how would you rate the handover from chatbot to human agent?
Scale: 1 – 7
Min:Very poorMax:Excellent
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- AI follow-ups automatically dig deeper when respondents report low satisfaction, uncovering root causes that static surveys miss
- Academic-grade scale construction with rubric-checked questions—no leading language or attention checks that bias results
- Every prompt, model, and logic branch is fully transparent and logged for reproducibility, unlike black-box competitor analytics
- AI interviewer dynamically follows up on churn reasons—if a customer says 'too expensive,' it probes whether that's absolute cost, perceived value, or competitive pricing
- Separate templates for exit diagnostic vs. win-back capture both the 'why they left' and 'what would bring them back' with distinct methodological approaches
SurveyMonkey
Customer Satisfaction Survey TemplateSurveyMonkey's flagship CSAT template is expert-certified and widely used, covering overall satisfaction, NPS, and CES together. It offers solid distribution channels (email, SMS, web links, QR codes) and built-in CSAT score calculation. However, it relies entirely on static pre-written questions with no adaptive probing.
What it does well
- Expert-certified methodology with built-in CSAT scoring formula and industry benchmarks
- Extensive distribution options including SMS, email, web links, and QR codes
- Large ecosystem with 400+ templates and cross-template metric comparison
Where it falls short
- No AI-powered follow-up questions—open-ended responses are passive, not probed
- Relies on demographic segmentation after the fact rather than real-time adaptive questioning
- Paid plans required for advanced features; Team plans range from $25-$75/user/month which adds up fast
Typeform
Top Customer Satisfaction Survey Questions & TemplateTypeform emphasizes a conversational, one-question-at-a-time interface designed to feel like a conversation rather than a form. Their CSAT template has good UX advice around avoiding bias and question timing, but ultimately all branching is pre-defined—there's no intelligent adaptation based on responses.
What it does well
- Beautiful conversational UI that asks one question at a time, boosting completion rates
- Strong guidance on avoiding biased language and proper survey timing
- 300+ integrations with tools like Slack, HubSpot, and Google Sheets
Where it falls short
- No AI follow-up capability—branching logic must be manually pre-configured for every path
- No prompt or model transparency; the 'conversational' feel is purely visual, not intelligent
- Limited methodological rigor—templates are light on proper academic scale construction
SurveySparrow
FREE Customer Satisfaction Survey TemplateSurveySparrow's CSAT template features a chat-like interface and claims 40% higher response rates. It includes recurring survey scheduling, multi-channel distribution, and conditional logic. However, its AI capabilities are limited to text analytics on collected responses rather than intelligent in-survey probing.
What it does well
- Chat-like conversational interface with claimed 40% higher response rates
- Recurring survey scheduling for automated pulse checks over time
- Conditional logic with skip/display rules to reduce survey fatigue
Where it falls short
- AI features limited to post-collection text analytics (CogniVue)—no in-survey AI follow-ups
- No transparency into how their AI text analytics models work or what prompts drive analysis
- Template questions are generic and not tailored to specific CX touchpoints like chatbot handoffs or checkout friction
Jotform
Online Shopping SurveyJotform's online shopping survey template is a basic form-builder approach—fully customizable with drag-and-drop, 100+ integrations, and free to use. It's functional but lacks any CX-specific methodology, AI capabilities, or sophisticated survey design principles.
What it does well
- Completely free with no-code drag-and-drop customization
- 100+ integrations including Google Drive, Dropbox, and Airtable
- Report Builder tool for analyzing responses visually
Where it falls short
- No AI-powered follow-ups or intelligent branching—purely static form fields
- No built-in CSAT scoring, CES calculation, or CX-specific methodology
- Generic shopping survey questions with no academic rigor or validated scale construction
Qualtrics
Customer Retention Survey Best PracticesQualtrics offers enterprise-grade CX measurement with advanced features like Predict iQ for churn prediction and conversational analytics. Their approach is the most sophisticated among competitors, but it comes at enterprise pricing that's prohibitive for academics and small teams, and their AI operates as a black box.
What it does well
- Predict iQ can analyze research data to predict which customers are about to churn
- Conversational analytics for understanding emotion, effort, intent and sentiment at scale
- Enterprise-grade action planning and closed-loop ticketing based on survey triggers
Where it falls short
- Enterprise pricing is prohibitive for academics, startups, and small CX teams
- AI analytics operate as a black box—no visibility into prompts, models, or logic flows
- Templates are gated behind sales conversations; no free self-serve template access for most CX use cases
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.